Pandas: How to Query a Column name with Spaces

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# Pandas: How to Query a Column name with Spaces

You can escape column names that contain spaces by wrapping them in backticks in a Pandas query() call.

The backtick character is right above the Tab key (to the left of the number 1 key).

main.py

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import pandas

df = pandas.DataFrame({
    'first name': ['Alice', 'Bobby', 'Carl', 'Alice'],
    'age': [30, 40, 50, 60],
    'net salary': [75, 50, 100, 150]
})

# ✅ With String values
result = df.query("`first name` == 'Alice'")

#   first name  age  net salary
# 0      Alice   30          75
# 3      Alice   60         150
print(result)


# ✅ Or with Integer Values
result = df.query("`net salary` > 50")

#   first name  age  net salary
# 0      Alice   30          75
# 2       Carl   50         100
# 3      Alice   60         150
print(result)

pandas query column name with spaces

The code for this article is available on GitHub

Notice that we wrapped the column name that contains spaces in backticks ``.

main.py

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result = df.query("`first name` == 'Alice'")

The backtick character is right above the Tab key on your keyboard.

The DataFrame.query() method enables us to query the columns of the DataFrame with a boolean condition.

The example above selects the rows that have a first name value of "Alice".

We wrapped the value in single quotes because it is a string.

main.py

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result = df.query("`first name` == 'Alice'")

Make sure to alternate between single and double quotes as shown in the code sample.

The whole query is wrapped in double quotes, so we used single quotes when wrapping the string value.

If the value is an integer, don't wrap it in quotes.

main.py

Copied!
import pandas

df = pandas.DataFrame({
    'first name': ['Alice', 'Bobby', 'Carl', 'Alice'],
    'age': [30, 40, 50, 60],
    'net salary': [75, 50, 100, 150]
})

result = df.query("`net salary` > 50")

#   first name  age  net salary
# 0      Alice   30          75
# 2       Carl   50         100
# 3      Alice   60         150
print(result)

dont wrap integer value in quotes

The code for this article is available on GitHub

Starting with Pandas version 0.25, the DataFrame.query() and DataFrame.eval() methods support quoting column names that contain spaces in backticks.

# In older Pandas versions, you had to use `DataFrame.loc`

In older Pandas versions, you had to use the DataFrame.loc label indexer because query() couldn't handle column names with spaces.

main.py

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import pandas

df = pandas.DataFrame({
    'first name': ['Alice', 'Bobby', 'Carl', 'Alice'],
    'age': [30, 40, 50, 60],
    'net salary': [75, 50, 100, 150]
})

result = df.loc[df['net salary'] > 50]

#   first name  age  net salary
# 0      Alice   30          75
# 2       Carl   50         100
# 3      Alice   60         150
print(result)

using dataframe loc in older pandas versions

The code for this article is available on GitHub

Make sure to use bracket notation and quote the column name as shown in the code sample.

We could shorten this a little by removing the .loc indexer to achieve the same result.

main.py

Copied!
import pandas

df = pandas.DataFrame({
    'first name': ['Alice', 'Bobby', 'Carl', 'Alice'],
    'age': [30, 40, 50, 60],
    'net salary': [75, 50, 100, 150]
})

result = df[df['net salary'] > 50]

#   first name  age  net salary
# 0      Alice   30          75
# 2       Carl   50         100
# 3      Alice   60         150
print(result)

removing the loc indexer

The code for this article is available on GitHub

The example uses boolean indexing instead of the query() method.

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Pandas: How to Query a Column name with Spaces

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